Hyperspectral imaging coupled with transformer enhanced convolutional autoencoder architecture towards real-time multi-target classification of damaged soybeans

Automated peanut defect detection using hyperspectral imaging and deep learning: A real-time approach for smart agriculture

Hyperspectral imaging (HSI) captures detailed spectral and spatial information, making it a versatile tool across various domains. This paper presents a Convolutional Neural Network (CNN)based method for vein detection from hyperspectral images of human hands. The proposed method applies a dimensionality reduction technique to the input HSI to extract its features and reduce its dimensionality. A CNN model is then trained and used to identify the location of vein pixels in the input image. Three dimensionality reduction methods, namely Principal Component Analysis (PCA), Folded-PCA (FPCA), and Inter-band Correlation and Clustering using the K-means clustering method (ICC_k-means), were used to reduce the dimensionality of the input image. A 3D-CNN model is then trained to detect the location of veins’ pixels in the input HSI. A 3D-CNN model was trained on dimensionality reduced HSI data to accurately identify the location of veins’ pixels in the input HSI. Experimental results were generated using the HyperVein dataset. The dataset images were randomly divided into training, validation, and test sets. Experimental results show that the proposed method using the ICC k-means dimensionality reduction technique achieves the highest accuracy, precision, recall, false positive rate, false negative rate, and receiver operating characteristics compared to when PCA and FPCA methods are used.

Visual attention mechanism has been widely used in computer vision and plays a vital role in the research of human action recognition. In this paper, we explore a novel moving target detection mechanism for human action recognition and propose a new 3D CNN (3D Convolutional Neural Network) model, dubbed LaM-2SRN (Local Features Enhanced and Moving target detected 2Stream-ResNet) for extracting and learning attention-enhanced spatiotemporal features. The contributions of this paper are as follow: First, the traditional CAM (Class Activation Map) based visual attention algorithm is used to obtain the optical flow information of the human region, thus eliminating the influence of irrelevant optical flow information (such as background clutter). Second, the ViBe algorithm is used to identify the moving target in the continuous frame, retain the optical flow information of the moving target, and make it complement with the optical flow information of the human region, to obtain a complete motion descriptor. After the motion information is marked in the video frame, we put the marked video frames and the original video frames into the 3D CNN and 2D CNN models respectively, to acquired mixed descriptor, which serves as the basis for video classification. Different from most of the previous target detection algorithms, this paper not only detects the human target, but also detects the moving foreground targets in the video, so that the pre-trained CNN model can obtain more complete motion information from videos. We use RGB-only video data for evaluation on two benchmarks: UCF101 and HMDB51, the experimental results demonstrate that our LaM-2SRN is comparable to the previous state-of-the-art algorithms.

Brain Age Estimation (BAE) has become a popular challenge in the field of medical and computer sciences in recent years. In the medical sciences field, the investigation on the brain structure and its relationship with aging is considerable. In the computer sciences field, creating an efficient Machine Learning (ML) model of BAE would lead to have accurate regression models. In this paper, a 3D Convolutional Neural Network (3D-CNN) model is used to train a brain age estimation system. To reach a more accurate system, two other regression methods are also applied on the final feature vector generated by 3D-CNN system. The system is applied on the samples of IXI dataset normalized by SPM14. Next, to ensure the model’s generalization, 47 healthy samples of ADNI1 dataset are used. Furthermore, some MRI images achieved from Alzheimer patients are feed to the proposed model and the effects of Alzheimer disease on brain aging are investigated. The best Mean Absolute Error (MAE) on evaluation dataset is about 5 years, with Root Mean Square Error (RMSE) = 13.5. The model generalization by a new healthy dataset was evaluated and the result is with the MAE value of about 6 years.

The Hyperspectral Images (HSI) are now being widely popular due to the evolution of satellite imagery and camera technology. Remote sensing has also gained popularity and it is also closely related to HSI. HSI possesses a wide variety of spatial and spectral features. However, HSI also has a consider-able amount of useless or redundant data. This redundant data causes a lot of trouble during classifications as it possesses a huge range in contrast to RGB. Traditional classification techniques do not apply efficiently to HSI. Even if somehow the traditional techniques are applied to it, the results produced are inefficient and undesirable. The Convolutional Neural Network (CNN), which are widely famous for the classification of images, have their fair share of trouble when dealing with HSI. 2D CNNs is not very efficient and 3D CNNs increases the computational complexity. To overcome these issues a new hybrid CNN approach is used which uses sigmoid activation function at the output layer, using a 2D CNN with 3D CNN to generate the desired output. Here, we are using HSI classification using hybrid CNN i.e., 2D and 3D. The dataset used is the Indian pines dataset sigmoid classifier for classification. And we gain the Overall accuracy 99.34 %, average accuracy 99.27%, kappa 99.25%.

Predicting seismic interaction effect between soil and structure group using convolutional neural network

Noninvasive assessment of skin structure using hyperspectral images has been intensively studied in recent years. Due to the high computational cost of the classical methods, such as the inverse Monte Carlo (IMC), much research has been done with the aim of using machine learning (ML) methods to reduce the time required for estimating parameters. This study aims to evaluate the accuracy and the estimation speed of the ML methods for this purpose and compare them to the traditionally used inverse adding-doubling (IAD) algorithm. We trained three models – an artificial neural network (ANN), a 1D convolutional neural network (CNN), and a random forests (RF) model – to predict seven skin parameters. The models were trained on simulated data computed using the adding-doubling algorithm. To improve predictive performance, we introduced a stacked dynamic weighting (SDW) model combining the predictions of all three individually trained models. SDW model was trained by using only a handful of real-world spectra on top of the ANN, CNN and RF models that were trained using simulated data. Models were evaluated based on the estimated parameters’ mean absolute error (MAE), considering the surface inclination angle and comparing skin spectra with spectra fitted by the IAD algorithm. On simulated data, the lowest MAE was achieved by the RF model (0.0030), while the SDW model achieved the lowest MAE on in vivo measured spectra (0.0113). The shortest time to estimate parameters for a single spectrum was 93.05 μs. Results suggest that ML algorithms can produce accurate estimates of human skin optical parameters in near real-time.

Exploring multi-b-value diffusion-weighted imaging for more accurate prostate segmentation and uncertainty estimation

Prediction of constrained modulus for granular soil using 3D discrete element method and convolutional neural networks

The convolutional neural network (CNN) is a potent and popular neural network types and has been crucial to deep learning in recent years. A standard CNN which is known as 2-dimensions CNN was first proposed to solve image classification problems at first. With advancements in science and technology, as well as the growth of the internet, tasks involving video analysis have received increased attention in recent years. As a result, it was suggested that a 3-dimensions convolutional neural network (3D CNN) be applied to tackle 3D image processing or video analysis. 3D CNN is primarily used in the field of human activity recognition, video classification, medical imaging detection, and so on. This paper offers a succinct introduction to the general architecture and representative models of 3D CNN, contrasts the variations between 2D CNN and 3D CNN, explores the widely used models derived from 3D CNN, and displays the application outcomes of 3D CNN.

Combining convolutional neural networks and on-line Raman spectroscopy for monitoring the Cornu Caprae Hircus hydrolysis process.

Advanced wind turbine blade inspection with hyperspectral imaging and 3D convolutional neural networks for damage detection

With the rapid development of hyper-spectral imaging techniques, hyper-spectral image classification has been applied to many tasks such as monitoring, astronomy and substance exploration. Hyper-spectral Images with rich spatial and spectral content is more difficult to be classified than common images with RGB channels. Many deep learning methods have ignored the context between spectral features when extracting spectral-spatial features of hyper-spectral images. So we implemented a 3D Convolution Neural Network model to extract correlated and effective features and improve the performance for Hyper-spectral Images classification. The hyper-spectral data set we use is the University of Pavia which has less training samples. So we exploited dropout and cross validation methods in the training process to avoid over fitting and we have extended the training samples by some transformation. The results of our experiments have shown that our model can generally get better results than some of the state-of-the-art methods.

Aim: The research study aims to detect the accuracy level of the pulmonary nodule using a convolutional neural network (CNN). The comparison between the Novel 3D CNN-fixed spatial transform algorithm and Novel 3D CNN Model algorithm for accurate detection. Materials and Methods: The information for this study was gained from the Kaggle website. The samples were taken into consideration as (N=20) for 3D CNN-fixed spatial transform and (N=20) 3D CNN Model according to the clinical. com, total sample size calculation was performed. Python software is used for accurate detection. Threshold Alpha is 0.05 %, G power is 80% and the enrollment ratio is set to 1. Result: This research study found that the 3D CNN with 89.29% of accuracy is preferred over 3D CNN with fixed spatial transform which gives 78.5% accuracy with a significance value (p=0.001), (p<0.05) with a 95% confidence interval. There is statistical significance between the two groups. Conclusion: The mean value of 3D CNN -fixed spatial transform is 78.5% and Novel 3D CNN is 89.29%.Novel 3D CNN appears to give better accuracy than 3D CNN-fixed spatial transform.

Fluvial floods endure as one of the most catastrophic weather-induced disasters worldwide, leading to numerous fatalities each year and significantly impacting socio-economic development and the environment. Hence, the research and development of new methods and algorithms focused on improving fluvial flood prediction and devising robust flood management strategies are essential. This study explores and assesses the potential application of 1D-Convolution Neural Networks (1D-CNN) for spatial prediction of fluvial flood in the Quang Nam province, a high-frequency tropical cyclone area in central Vietnam. To this end, a geospatial database with 4156 fluvial flood locations and 12 flood indicators was considered. The ADAM algorithm and the MSE loss function were used to train the 1D-CNN model, whereas popular performance metrics, such as Accuracy (Acc), Kappa, and AUC, were used to measure the performance. The results indicated remarkable performance by the 1D-CNN model, achieving high prediction accuracy with metrics such as Acc = 90.7%, Kappa = 0.814, and AUC = 0.963. Notably, the proposed 1D-CNN model outperformed benchmark models, including DeepNN, SVM, and LR. This achievement underscores the promise and innovation brought by 1D-CNN in the realm of susceptibility mapping for fluvial floods.